1. • Radar captures objects within 200m ahead and ±40m laterally
• Data filtration use to identify steady targets that:
– Are within 2.25 m laterally, assuming that the standard lane width is 4.5 m (15 ft.).
– Consecutive record of the object for at least 10 seconds to avoid “ghost target” records.
– Headway gap change <2 m/s (<5 mph) to be considered as steady state car following.
• Clear relation between driving speed and headway
– At low speeds to <70 km/h nearly linear
– At higher speeds >80 km/h average headway flattens to 40m
Analysis of the Headway Distance from a Subset of SHRP2
Research Assistant Professor, Department of Automotive Engineering, Clemson University, Greenville, SC
Dr. Andrej IVANCO
• Autonomous vehicles are at the verge of
adoption
• Adoption rate depends on transparency to
the end user
INTRODUCTION
OBJECTIVES
ACKNOWLEDGEMENTS
• This research was graciously supported by
Ford Motor Company University
Research Program (Ford - URP)
Headway
Radar
DATA SOURCE
• Radar data from ~3,800 trips analyzed
from SHRP2
• Trip duration of 17-24 minutes to assure
consistent spectrum of road conditions
• Overall about 20 timestamped data
channels from:
• Radar:
– Range headway and lateral
– Left and right lane distance
• Gyroscope
– Accelerations and angular
velocities
• Vehicle network
– Vehicle speed
METHOD
RESULTS
REFERENCES
CONCLUSION
Figure 1: Radar data example Figure 2: Speed vs. Headway distance
Figure 4: Vehicle specific headwayFigure 3: Aggregated headway
• Analyze SHRP2 data and identify the
naturalistic driving behavior
• Case one - headway distance
35
mph
45
mph
55
mph
65
mph
• Headway distance can be a signature for
the driving style
• Tree distinct driving behavior identified
– Cautious
– Average
– Aggressive
• Open topics, influence of:
– Traffic Conditions
– Driving environment, city vs rural
– Driving situation, off-ramp,
merging, etc.
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• Gorman, T., L. Stowe, and J. Hankey, S31: NDS
Data Dissemination Activities , Task 1.6: Radar
Post-Processing. 2015.